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assist in developing quantum reservoir computing algorithms and work using them to model power systems data from, in particular, NLR’s ARIES platform, to explore the limits of quantum reservoir computing
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an opening for a graduate student researcher in Quantum Computing with special emphasis on algorithm design for materials science applications. Quantum computing promises to enable large materials
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Science Center has an opening for a graduate student researcher in Mathematical Optimization for large-scale power systems planning. They will deploy developed optimization algorithms on DOE high
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an opening for a graduate student researcher in Quantum Algorithms with emphasis on optimization. We are looking for a dynamic, motivated researcher with a strong technical background and an interest
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and test Learning-to-Optimize (L2O) algorithms for accelerating large-scale optimization. Perform computational experiments on benchmark power system datasets. Participate in weekly research meetings
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uncertainty/sensitivity analysis. Develop and apply predictive models, machine learning algorithms, and data‑driven analytical methods. Support the development of generalizable, scalable physical system models
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at least one of: High-Performance Computing Distributed Systems Parallel Computing High Performance Algorithms Multiple Linux distributions · Experience with SLURM or similar HPC schedulers
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metrics for large-scale, unbalanced distribution networks. Designing and implementing advanced dynamic state estimation algorithms (EKF, UKF, MHE, WLS-based methods) for measurement-constrained distribution
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grid planning. Design and code efficient algorithms for large-scale optimization problems using the Julia programming language and packages such as JuMP.jl. Experience with Xpress and Gurobi are a plus
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systems engineering, electrical engineering, or other relevant areas Have a good fundamental knowledge of neural networks, state-of-the-art learning algorithms, and their applications to complex systems